Overcoming Industry Challenges Through Digital Mining

November 05, 2019 by Stefanini

Mining involves a complex process of extraction and processing minerals that are pulled from beneath the Earth’s surface. Faced with increasing stakeholder expectations, the global mining industry faces significant challenges due to plummeting commodity prices, lessening global demand and increasing safety and security risks. Among the hurdles to overcome include new government oversight, safety concerns, and the process’ high cost.

While facing these challenges, miners must also look to improve safety, scale operations, improve operational processes, and enhance production, thereby driving the need for new business models and operating models. Today, miners are embracing the high computing power of digital technologies, including cloud-enabled mobility, big data-powered analytics and the industrial Internet of Things (IoT). Through digital innovation, companies will improve production and long term operational excellence.

One of the most notable trends affecting mining is the fact that the industry is largely in decline. As society has become reliant on other types of energy consumption, there has been less of a need for fossil fuels like coal. According to McKinsey, global mining productivity has declined 3.5 percent a year over the past decade. This trend pervades across commodities, geographies, and most mining companies.

The Benefits of Digital Mining for Companies

One of the most obvious benefits of emerging technologies for mining is the fact that they can be used to mitigate variability caused by external forces – such as situations created by the miners themselves. This is why the industrial internet – which leverages the power of smart machines and real-time analytics to take advantage of the data that machines produce in industrial settings – is so crucial for the future of mining.

More and more industry professionals are turning to technologies like digital twins to virtually simulate mining operations. Rather than leave situations to chance, management can manipulate a number of variables over a given time period to see how changes will affect both up-and down-stream processes. By giving teams the ability to simulate changes in the mining process, quickly and with no risk to the actual (rather costly) operation, the pressure associated with making mistakes is removed.

Identifying Four Different Groups of Technology

McKinsey outlines four different clusters of technology that are accelerating. The first group is data, computing power, and connectivity. An increasingly affordable and accessible option, physical objects can be embedded with sensors, which report large volumes of data to analyze and enable communications among machines. Sensor data is already produced by miners, potentially allowing them to gain a more realistic and dependable view of the rock face.

The next cluster that McKinsey pinpoints is analytics and intelligence. McKinsey notes that advances in big data analytics, from machine learning to improved statistical techniques for integrating data, turn vast data sets into insight about the probability of future events. Smart statistical and optimization algorithms can increasingly be used for complex mining tasks like geological modeling, on-the-day scheduling, and predictive maintenance.

Human-machine interaction is increasingly a normal part of day-to-day life. Think about the way you use your own smartphone – for driving directions, interaction with social media, and checking emails – you’d be lost without it. The industrial field is also making use of applications. McKinsey reports several examples, including “smart” glasses or goggles that send instructions to workers operating on assembly lines or making repairs on equipment, which improves operating models. Another example includes work clothing that has sensors embedded in it that securely transmits data to managers about hazardous conditions and the workers’ physical conditions, which improves safety overall.

The final cluster investigated by McKinsey is digital-to-physical conversion. Thanks to advances in robotics, more fully autonomous equipment has become affordable and effective. McKinsey cites the fact that in manufacturing, for instance, the cost of industrial robots has fallen by 50 percent since 1990. In contrast, US labor costs have risen 80 percent over the same period. Artificial intelligence has increased the sophistication of robotics and assisted-control equipment, further leading to technological innovation. In haulage, drilling, and other processes, the deployment of fully autonomous equipment is becoming more common, along with the use of teleremote and assisted-control equipment.

These technologies mark a significant shift in the way mining works. They will help reduce variability in decision making and deploy more centralized, mechanized operations to reduce instability in execution.

Five Sites of Digitization

When it comes to the future of mining, McKinsey cites five areas of digitization that will create significant value:

1.       Deep understanding of the resource base – one of the best ways mining executives can potentially gain insight into their industry is to know what exactly is in the ground and where those materials are placed. McKinsey advises that the journey of developing resource insight, from exploration to short-term mine planning, is often scattered across organizational boundaries, data sources, and different geological models. By combining ore-body model information with blast-hole drill data and online sampling, miners can gain much better resource knowledge. Exploration data has the potential through statistical techniques to improve the probability of discovery and help target further drilling to maximize information gains. To help optimize drill and blast patterns, create an executable mine plan, and avoid quality issues at the source, mining companies should integrate geological information into one better, universal source of truth. Using equipment like those listed, traditional activities like core logging, face inspections, and manual plant assays are no longer necessary.

2.       Optimization of material and equipment flow – Mining supply chains are a collective system of many parts of fixed and mobile equipment. While tools and metrics like OEE can lay the groundwork for operational improvement, they might fail to understand system complexity. Real time data and better analytical engines are allowing the scheduling and processing decisions that maximize utilization of equipment and yields. One example McKinsey cites is in the mine pit. Here, combining traditional dispatch with smart algorithms can advance machine movements for peak adaptability. Another is in processing plants, where potentially, many plant operators do not full understand the drivers of yield. By applying new mathematical techniques that look for hidden relationships between second- and third-order variables, McKinsey has found that plant yields of gold, nickel, phosphate, and other processed minerals can often be improved by 3 to 10 percent quickly and efficiently.

3.       Improved anticipation of failures – Drills, trucks, processing plants and trains generate huge amounts of data that can be collected by mining companies. However, this information isn’t properly utilize by these companies as it often is not used to generate insight. McKinsey notes that miners use less than 1 percent of the information collected from their equipment. To reduce maintenance spending and present unplanned interruptions that cost metric tons, utilize this information to estimate the probability of failure of specific components.

4.       Increased mechanization through automation – automation offers a plethora of potential benefits, including reducing operating costs, improving operating discipline, and making working conditions safer for employees. Technologies like automated haulage and drilling have moved into full-scale commercialization. Others, specifically automated blasting and shoveling, are in testing. McKinsey’s analysis suggests that the economics of haulage are concrete – reducing total cost of ownership by between 15 and 40 percent. However, it notes that miners must manage several design choices around pit configuration, equipment configuration, and operational transitions. Further, McKinsey has identified opportunities to reduce the number of people working in dangerous areas by more than 50 percent. Clearly, automation is a trend the mining industry needs to pay attention to.

5.       Monitoring of real-time performance vs. plan – real-time data allows users to know the state and location of all pieces of equipment in a mining operation at every second. This allows users to realize whether or not the equipment is operating according to or outside the plan. When it comes to management systems, real-time insight allows mining companies to focus less on monthly output and more on variability and compliance to plan. Further, this real-time feed makes in-the-moment responses possible; by connecting with a central operating center, the control is moved to a more sophisticated decision-making capability at the center that can take actions to optimize operations across the whole supply chain instead of local silos. Safety outcomes, therefore, will be improved by detecting deviations from expected operating conditions. Further, this capability can be deployed to maintain high equipment utilization and low operating cost that is consistent with operating plans.

Mining is an arduous, complicated process, but through the use of digital technologies, can be made more efficient. At Stefanini, we know and recognize the industry’s challenges and how to overcome them.

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